1. Field of the Invention
The present invention generally relates to mobile digital communications systems such as air-to-ground and cellular communications systems and, more particularly, to a technique for (1) suppressing background noise when the speaker is not talking and (2) allowing speech signals to pass freely.
2. Description of the Prior Art
In mobile telephone applications such as cellular and air-to-ground telephony systems, background noise can be a hindrance to the conversation and its intelligibility. It is generally desirable to implement some form of noise suppression for the purpose of increasing the intelligibility of the mobile speaker's voice by allowing the person at the other end of the conversation to not to have to listen to a high audio level of background noise in pauses and silent periods of the conversation.
A voice activity detector (VAD) is used to detect speech for noise suppression. Accurate voice activity detection is important to permit reliable detection of speech in a noisy environment and therefore affects system performance and the quality of the received speech. Prior art VAD algorithms which analyze spectral properties of the signal suffer from high computational complexity. Simple VAD algorithms which look at short term time characteristics only in order to detect speech do not work well with high background noise.
There are basically two approaches to detecting voice activity. The first are pattern classifiers which use spectral characteristics that result in high computational complexity. An example of this approach uses five different measurements on the speech segment to be classified. The measured parameters are the zero-crossing rate, the speech energy, the correlation between adjacent speech samples, the first predictor coefficient from a 12-pole linear predictive coding (LPC) analysis, and the energy in the prediction error. This speech segment is assigned to a particular class (i.e., voiced speech, un-voiced speech, or silence) based on a minimum-distance rule obtained under the assumption that the measured parameters are distributed according to the multidimensional Gaussian probability density function.
The second approach examines the time domain characteristics of speech. An example of this approach implements an algorithm that uses a complementary arrangement of the level, envelope slope, and an automatic adaptive zero crossing rate detection feature to provide enhanced noise immunity during periods of high system noise.